The IASO Self-Reporting System

Mood swings are commonly observed phenomena among hospitalized patients. As a result, there has been a growing interest in developing solutions that can assist caregivers in acquiring a better understanding of patient mood states and behaviors. A key challenge resides in the need to not only monitor patients\u27 mood state but also to try to influence it and regulate it. This article presents the IASO self-reporting system, a persuasive clinical mood tracking, and a management application for hospital patients. We describe the design process of the system, its technical implementation details, and key features. Unlike most earlier related studies, IASO incorporates the concept of mood-based adaptive art (MBAA) that triggers animated digital art clips with background sounds in response to patients\u27 self-reported mood states, thus offering a tool for creative healing and mood enhancement. Our proposed solution empowers patients to gain more control over their wellbeing, regulates their moods and enables caregivers to receive timely feedback about potential mood swings and dangerous mood conditions

Towards a Technology of Nonverbal Communication

Nonverbal communication is the main channel through which we experience inner life of others, including their emotions, feelings, moods, social attitudes, etc. This attracts the interest of the computing community because nonverbal communication is based on cues like facial expressions, vocalizations, gestures, postures, etc. that we can perceive with our senses and can be (and often are) detected, analyzed and synthesized with automatic approaches. In other words, nonverbal communication can be used as a viable interface between computers and some of the most important aspects of human psychology such as emotions and social attitudes. As a result, a new computing domain seems to emerge that we can define “technology of nonverbal communication”. This chapter outlines some of the most salient aspects of such a potentially new domain and outlines some of its most important perspectives for the future

Automatic Facial Expression Recognition by Facial Parts Location with Boosted-LBP

International audienceThis paper proposes an automatic facial expression recognition system, which uses new methods in both face detection and feature extraction. In this system, considering that facial expressions are related to a small set of muscles and limited ranges of motions, the facial expressions are recognized by these changes in video sequences. First, the differences between neutral and emotional states are detected. Faces can be automaticallylocated from changing facial organs. Then, LBP features are applied and AdaBoost is used to find the most important features for each expression on essential facial parts. At last, SVM with polynomial kernel is used to classify expressions. The method is evaluated on JAFFE and MMI databases. The performances are better than other automatic or manual annotated systems

Affective tutoring systems: Enhancing e-learning with the emotional awareness of a human tutor

This paper introduces the field of affective computing, and the benefits that can be realized by enhancing e-learning applications with the ability to detect and respond to emotions experienced by the learner. Affective computing has potential benefits for all areas of computing where the computer replaces or mediates face to face communication. The particular relevance of affective computing to e-learning, due to the complex interplay between emotions and the learning process, is considered along with the need for new theories of learning that incorporate affect. Some of the potential means for inferring users’ affective state are also reviewed. These can be broadly categorized into methods that involve the user’s input, and methods that acquire the information independent of any user input. This latter category is of particular interest as these approaches have the potential for more natural and unobtrusive implementation, and it includes techniques such as analysis of vocal patterns, facial expressions or physiological state. The paper concludes with a review of prominent affective tutoring systems and promotes future directions for e-learning that capitalize on the strengths of affective computing